Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

American College of Radiology (ACR) CEO Dana H. Smetherman, MD, MPH, MBA, FACR, explains why opportunistic screening is an important AI imaging technology trend radiology practices should be paying attention.

AI opportunistic screening may have tremendous potential to help patients, ACR CEO says

American College of Radiology leader Dana Smetherman, MD, MBA, discusses the new technology trend and why radiologists should be paying attention. 

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Generative AI explains echo results to heart patients

Researchers used OpenAI’s ChatGPT technology to build 100 patient-friendly echo reports, evaluating each one for accuracy, relevance and understandability. The AI reports passed the test with flying colors. 

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Automated CT-derived markers identify those at higher risk of developing diabetes and other conditions

These measures could be utilized as an opportunistic screening tool in individuals who undergo routine health screenings.

As the pregnancy advances, the embryo is easier to see, and accurate measurements can be obtained with transabdominal ultrasound. A crown-rump length (CRL) measurement. This ultrasound scan shows the CRL, which is the average of discrete fetal measurements from the tip of the head end to the tip of the rump end in the midsagittal plane of the embryo. This is highly accurate for pregnancy dating. Images courtesy of RSNA

1 day of training with AI makes novice sonographers as accurate as experts in gestational measurements

This sort of tool could be a game changer for prenatal care in low resource settings, authors of a new paper published in JAMA suggest. 

AI tool detects PE missed by rads

Commercially available AI tool detects 76% of PE that rads initially missed on CT

Improving the detection of pulmonary embolism has been a popular target among multiple AI vendors.

Dana H. Smetherman, MD, MPH, MBA, FACR, the new CEO of the American College of Radiology (ACR), explains some of the hot button issues in radiology and advocacy efforts led by the ACR.

New ACR CEO outlines key concerns for radiology

Dana Smetherman, MD, MBA, explains some of the hot button issues in imaging and key advocacy efforts being undertaken by the college. 

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CMS to reimburse providers for use of AI prostate cancer mapping tool

Experts believe that these reimbursements will begin to gain steam as more organizations turn to AI to help address some of healthcare’s biggest pain points, such as staffing shortages. 

AI helps identify malignant/benign pancreatic lesions

Multimodal AI model helps differentiate between benign and malignant pancreatic lesions

Endoscopic ultrasonography has emerged as a valuable tool for diagnosing pancreatic cancer, but its specificity in differentiating between benign and malignant pancreatic lesions varies.

Around the web

The nuclear imaging isotope shortage of molybdenum-99 may be over now that the sidelined reactor is restarting. ASNC's president says PET and new SPECT technologies helped cardiac imaging labs better weather the storm.

CMS has more than doubled the CCTA payment rate from $175 to $357.13. The move, expected to have a significant impact on the utilization of cardiac CT, received immediate praise from imaging specialists.

The newly cleared offering, AutoChamber, was designed with opportunistic screening in mind. It can evaluate many different kinds of CT images, including those originally gathered to screen patients for lung cancer. 

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